Neha Gyawali
4/26/2022
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Warning: package 'usmap' was built under R version 4.1.3
## Warning: package 'plotly' was built under R version 4.1.3
## Rows: 7246 Columns: 17
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): name, date, manner_of_death, armed, gender, race, city, state, thr...
## dbl (4): id, age, longitude, latitude
## lgl (3): signs_of_mental_illness, body_camera, is_geocoding_exact
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## New names:
## * `` -> ...9
## Rows: 25 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): City
## dbl (1): Other
## lgl (1): ...9
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Here we can see the difference in the number of female versus male fatalities. This helps us understand the demographic that is in danger of being killed by a police officer.
## # A tibble: 3 x 2
## gender count
## <chr> <int>
## 1 F 327
## 2 M 6913
## 3 <NA> 6
Some text about this table and how interesting the results are!
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
## Warning: Ignoring unknown aesthetics: text
Using these graphs we can see the top 25 cities with the most police
fatalities. The first graph shows us the race breakdown in numbers and
the second one shows us the race breakdown in percentage.
## # A tibble: 7,246 x 18
## # Groups: date [2,471]
## id name date manner_of_death armed age gender race city state
## <dbl> <chr> <date> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 3 Tim El~ 2015-01-02 shot gun 53 M Asian Shel~ WA
## 2 4 Lewis ~ 2015-01-02 shot gun 47 M White Aloha OR
## 3 5 John P~ 2015-01-03 shot and Taser~ unar~ 23 M Hisp~ Wich~ KS
## 4 8 Matthe~ 2015-01-04 shot toy ~ 32 M White San ~ CA
## 5 9 Michae~ 2015-01-04 shot nail~ 39 M Hisp~ Evans CO
## 6 11 Kennet~ 2015-01-04 shot gun 18 M White Guth~ OK
## 7 13 Kennet~ 2015-01-05 shot gun 22 M Hisp~ Chan~ AZ
## 8 15 Brock ~ 2015-01-06 shot gun 35 M White Assa~ KS
## 9 16 Autumn~ 2015-01-06 shot unar~ 34 F White Burl~ IA
## 10 17 Leslie~ 2015-01-06 shot toy ~ 47 M Black Knox~ PA
## # ... with 7,236 more rows, and 8 more variables:
## # signs_of_mental_illness <lgl>, threat_level <chr>, flee <chr>,
## # body_camera <lgl>, longitude <dbl>, latitude <dbl>,
## # is_geocoding_exact <lgl>, count <int>